| Variable | Overall |
|---|---|
| n | 30 |
| Age (mean (sd)) | 39.27 (10.12) |
| sex = M (%) | 7 (23.3) |
| EDSS (mean (sd)) | 2.61 (1.88) |
| Lesion_Volume (mean (sd)) | 17.40 (16.13) |
| MS_Subtype (%) | |
| Clinically Isolated Syndrome | 2 (6.7) |
| Progressive-relapsing | 1 (3.3) |
| Relapsing-remitting | 24 (80.0) |
| Secondary-progressive | 2 (6.7) |
| Unspecified | 1 (3.3) |
The N4 (Tustison et al. 2010) EM-style model assumed is: \[ \log(x(v)) = \log(u(v)) + \log( f(v) ) \]
Figure from Multi-Atlas Skull Stripping method paper (Doshi et al. 2013):
smri.processmuschellij2/smri.process)Training Data Structure
10% of the voxels (save computation time)Let \(y_{i}(v)\) be the presence / absence of lesion for voxel \(v\) from person \(i\).
General model form: \[
P(Y_{i}(v) = 1) \propto f(X_{i}(v))
\]
- Previous work - OASIS (Sweeney et al. 2013):
\[ f(X_{i}(v)) = \text{expit} \left\{ \beta_0 + \sum_{k} x_{k}(v)\beta_{k} + x_{k}(v) \times x_{10, k} \beta_{10,k} + x_{k}(v) \times x_{20, k} \beta_{20,k}\right\} \]
\(k \in \{T1, T2, FLAIR, PD\}\).
For each model (RF with and w/o T1Post and OASIS retrained or not)
For each test scan, and over all test scans, we can calculate the following 2-by-2 table, where cells represent number of voxels and corresponding Venn diagram:
| Manual | |||
| 0 | 1 | ||
| Auto | 0 | TN | FN |
| 1 | FP | TP | |
Dice Coeffiicent (Dice 1945): \[
\text{Dice} = \frac{2\times\text{TP}}{2\times\text{TP} + \text{FN} + {FP}}
\]
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer:5–32.
Dice, Lee R. 1945. “Measures of the Amount of Ecologic Association Between Species.” Ecology 26 (3):297–302. http://www.jstor.org/stable/1932409.
Doshi, Jimit, Guray Erus, Yangming Ou, Bilwaj Gaonkar, and Christos Davatzikos. 2013. “Multi-Atlas Skull-Stripping.” Academic Radiology 20 (12). Elsevier:1566–76.
Lesjak, Žiga, Alfiia Galimzianova, Aleš Koren, Matej Lukin, Franjo Pernuš, Boštjan Likar, and Žiga Špiclin. 2018. “A Novel Public MR Image Dataset of Multiple Sclerosis Patients with Lesion Segmentations Based on Multi-Rater Consensus.” Neuroinformatics 16 (1). Springer:51–63.
Sweeney, Elizabeth M, Russell T Shinohara, Navid Shiee, Farrah J Mateen, Avni A Chudgar, Jennifer L Cuzzocreo, Peter A Calabresi, Dzung L Pham, Daniel S Reich, and Ciprian M Crainiceanu. 2013. “OASIS Is Automated Statistical Inference for Segmentation, with Applications to Multiple Sclerosis Lesion Segmentation in MRI.” NeuroImage: Clinical 2. Elsevier:402–13.
Tustison, Nicholas J., Brian B. Avants, Philip A. Cook, Yuanjie Zheng, Alexander Egan, Paul A. Yushkevich, and James C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29 (6):1310–20. https://doi.org/10.1109/TMI.2010.2046908.
Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1):1–17. https://doi.org/10.18637/jss.v077.i01.
Zhang, Yongyue, Michael Brady, and Stephen Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” Medical Imaging, IEEE Transactions on 20 (1):45–57. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=906424.